1. Field
Embodiments discussed herein relate to a markerless augmented reality system and method for extracting feature points within an image and providing augmented reality using a projective invariant of the feature points.
2. Description of the Related Art
Augmented Reality (AR) refers to a technology of inserting a virtual graphic (object) into an actual image acquired by a camera and generating an image in which a real object and a virtual object are mixed. AR is characterized in that supplementary information using a virtual graphic may be provided onto an image acquired in the real world. Such AR is used in fields such as educational materials, road guidance or games and also is used as a user interface. In order to provide natural AR, three-dimensional positional information of a camera to acquire an image is rapidly and accurately detected. To this end, marker-based augmented reality using a square marker with a high contrast ratio has come into wide use. In this case, four corner points of a square are detected using the square marker and three-dimensional camera information is computed using this information. The AR using the marker which is easily detected is advantageous in that recognition and tracking are relatively accurately performed in real time. However, actual sensation is decreased due to the use of the artificial marker and heterogeneous sensation is given to a user.
For this reason, recently, research into AR using a general natural image instead of the artificial marker is ongoing. In general, markerless AR uses a feature point matching method. Feature point matching refers to an operation for searching for and connecting the same feature points in two difference images. A method for extracting a plane using a Simultaneous Localization and Map-building (SLAM)/Parallel Tracking And Mapping (PTAM) algorithm for tracking three-dimensional positional information of a camera and three-dimensional positional information of feature points in real time and providing AR using the plane has been suggested. However, since the SLAM/PTAM algorithm acquires the image so as to search for the feature points, computes the three-dimensional position of the camera and the three-dimensional positions of the feature points, and provides AR based on such information, a considerable computation is necessary.